Python:为数据列表找到最合适的函数 [英] Python: Find a best fit function for a list of data

问题描述

我知道通过random模块内置的许多python内置概率函数.

I am aware of many probabilistic functions builted-in python, with the random module.

我想知道，给定一个浮点数列表，是否有可能找到最适合该列表的分布方程?

I'd like to know if, given a list of floats, would be possible to find the distribution equation that best fits the list?

我不知道numpy是否这样做，但是可以将该功能与Excel的趋势"功能进行比较(不相等，但类似).

I don't know if numpy does it, but this function could be compared (not equal, but similar) with the Excel's "Trend" function.

我该怎么做?

推荐答案

查看 numpy.polyfit

numpy.polyfit(x, y, deg, rcond=None, full=False)

最小二乘多项式拟合.

Least squares polynomial fit.

拟合多项式p(x)= p [0] * x ** deg + ... + p°度deg (x，y).返回系数p的矢量，该矢量使平方误差最小.

Fit a polynomial p(x) = p[0] * x**deg + ... + p[deg] of degree deg to points (x, y). Returns a vector of coefficients p that minimises the squared error.

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